Multi-Objective Optimization of Multi-Product Parallel Disassembly Line Balancing Problem Considering Multi-Skilled Workers Using a Discrete Chemical Reaction Optimization Algorithm

This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers.A mathematical model for the parallel disassembly line is established to achieve maximized disassembly profit and minimized workstation cycle time.Based on a product’s AND/OR graph,matrices for task-skill,worker-skill,precedence relationships,and disassembly correlations are developed.A multi-objective discrete chemical reaction optimization algorithm is designed.To enhance solution diversity,improvements are made to four reactions:decomposition,synthesis,intermolecular ineffective collision,and wall invalid collision reaction,completing the evolution of molecular individuals.The established model and improved algorithm are applied to ball pen,flashlight,washing machine,and radio combinations,respectively.Introducing a Collaborative Resource Allocation(CRA)strategy based on a Decomposition-Based Multi-Objective Evolutionary Algorithm,the experimental results are compared with four classical algorithms:MOEA/D,MOEAD-CRA,Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ),and Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ).This validates the feasibility and superiority of the proposed algorithm in parallel disassembly production lines.

Digital Twin Modeling and Simulation Optimization of Transmission Front and Middle Case Assembly Line

As the take-off of China’s macro economy,as well as the rapid development of infrastructure construction,real estate industry,and highway logistics transportation industry,the demand for heavy vehicles is increasing rapidly,the competition is becoming increasingly fierce,and the digital transformation of the production line is imminent.As one of themost important components of heavy vehicles,the transmission front andmiddle case assembly lines have a high degree of automation,which can be used as a pilot for the digital transformation of production.To ensure the visualization of digital twins(DT),consistent control logic,and real-time data interaction,this paper proposes an experimental digital twin modeling method for the transmission front and middle case assembly line.Firstly,theDT-based systemarchitecture is designed,and theDT model is created by constructing the visualization model,logic model,and data model of the assembly line.Then,a simulation experiment is carried out in a virtual space to analyze the existing problems in the current assembly line.Eventually,some improvement strategies are proposed and the effectiveness is verified by a new simulation experiment.

Effective Hybrid Teaching-learning-based Optimization Algorithm for Balancing Two-sided Assembly Lines with Multiple Constraints

Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints （TALB-MC）. Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization （TLBO） algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost fimction. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search （VNS） is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm （LAHC） for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.

Optimization of assembly line balancing using genetic algorithm

In a manufacturing industry, mixed model assembly line(MMAL) is preferred in order to meet the variety in product demand. MMAL balancing helps in assembling products with similar characteristics in a random fashion. The objective of this work aims in reducing the number of workstations, work load index between stations and within each station. As manual contribution of workers in final assembly line is more, ergonomics is taken as an additional objective function. Ergonomic risk level of a workstation is evaluated using a parameter called accumulated risk posture(ARP), which is calculated using rapid upper limb assessment(RULA) check sheet. This work is based on the case study of an MMAL problem in Rane(Madras) Ltd.(India), in which a problem based genetic algorithm(GA) has been proposed to minimize the mentioned objectives. The working of the genetic operators such as selection, crossover and mutation has been modified with respect to the addressed MMAL problem. The results show that there is a significant impact over productivity and the process time of the final assembled product, i.e., the rate of production is increased by 39.5% and the assembly time for one particular model is reduced to 13 min from existing 18 min. Also, the space required using the proposed assembly line is only 200 m2 against existing 350 m2. Further, the algorithm helps in reducing workers fatigue(i.e., ergonomic friendly).

OPTIMIZATION OF ACOUSTIC IMPEDANCE，GEOMETRIC STRUCTURE AND OPERATING CONDITION OF LINERS MOUNTED IN ENGINE DUCT

Acoustically absorptive treatment in aircraft engine nacelle is an essential part of the overall aircraft noise reduction effort. The investigation on the optimization of multi-liners plays an important role in noise reduction. Based upon the mode analysis method of sound propagation in a circular duct with multiple liners, a flexible tolerance method is used to optimize the acoustic parameters(impedance), geometric structure parameters(such as open area ratio, cavity depth and hole diameter) and operating condition parameters(such as blade passing frequency). The mathematical models for these kinds of optimization are presented here. The optimum values of the design variables are determined when the in-duct sound suppression approaches a maximum. It can be derived from the optimum results that the emphasis of the engineering optimization design of the perforated plate honey-comb structure should be placed on the optimum choice of the open area ratio and cavity depth. Some reference criteria for the engineering design of the multi-linings are also provided.

An Optimization Approach of IoD Deployment for Optimal Coverage Based on Radio Frequency Model

Recently,Internet of Drones(IoD)has garnered significant attention due to its widespread applications.However,deploying IoD for area coverage poses numerous limitations and challenges.These include interference between neighboring drones,the need for directional antennas,and altitude restrictions for drones.These challenges necessitate the development of efficient solutions.This research paper presents a cooperative decision-making approach for an efficient IoDdeployment to address these challenges effectively.The primary objective of this study is to achieve an efficient IoDdeployment strategy thatmaximizes the coverage regionwhile minimizing interference between neighboring drones.In deployment problem,the interference increases as the number of deployed drones increases,resulting in bad quality of communication.On the other hand,deploying a few drones cannot satisfy the coverage demand.To accomplish this,an enhanced version of a concise population-based meta-heuristic algorithm,namely Improved Particle SwarmOptimization(IPSO),is applied.The objective function of IPSO is defined based on the coverage probability,which is primarily influenced by the characteristics of the antennas and drone altitude.A radio frequency(RF)model is derived to evaluate the coverage quality,considering both Line of Sight(LOS)and Non-Line of Sight(NLOS)down-link coverage probabilities for ground communication.It is assumed that each drone is equipped with a directional antenna to optimize coverage in a given region.Extensive simulations are conducted to assess the effectiveness of the proposed approach.Results demonstrate that the proposed method achieves maximum coverage with minimum transmission power.Furthermore,a comparison is made against Collaborative Visual Area Coverage Approach(CVACA),and a game-based approach in terms of coverage quality and convergence speed.The simulation results reveal that our approach outperforms both CVACA and the gamebased schemes in terms of coverage and convergence speed.Comparisons validate the superiority of our approach over existing methods.To assess the robustness of the proposed RFmodel,we have considered two distinct ranges of noise:range1 spanning from−120 to−90 dBm,and range2 spanning from−90 to−70 dBmfor different numbers of UAVs.In summary,this research presents a cooperative decision-making approach for efficient IoD deployment to address the challenges associatedwith area coverage and achieves an optimal coveragewithminimal interference.

An efficient migrating birds optimization algorithm with idle time reduction for Type-I multi-manned assembly line balancing problem

Multi-manned assembly line,which is broadly utilized to assemble high volume products such as automobiles and trucks,allows a group of workers to assemble different tasks simultaneously in a multi-manned workstation.This additional characteristic of parallel operators increases the complexity of the traditional NP-hard assembly line balancing problem.Hence,this paper formulates the Type-I multi-manned assembly line balancing problem to minimize the total number of workstations and operators,and develops an efficient migrating birds optimization algorithm embedded into an idle time reduction method.In this algorithm,a new decoding mechanism is proposed which reduces the sequence-dependent idle time by some task assignment rules;three effective neighborhoods are developed to make refinement of existing solutions in the bird improvement phases;and temperature acceptance and competitive mechanism are employed to avoid being trapped in the local optimum.Comparison experiments suggest that the new decoding and improvements are effective and the proposed algorithm outperforms the compared algorithms.

Optimization of Line Configuration and Balancing for Flexible Machining Lines

Line configuration and balancing is to select the type of line and allot a given set of operations as well as machines to a sequence of workstations to realize high-efficiency production. Most of the current researches for machining line configuration and balancing problems are related to dedicated transfer lines with dedicated machine workstations. With growing trends towards great product variety and fluctuations in market demand, dedicated transfer lines are being replaced with flexible machining line composed of identical CNC machines. This paper deals with the line configuration and balancing problem for flexible machining lines. The objective is to assign operations to workstations and find the sequence of execution, specify the number of machines in each workstation while minimizing the line cycle time and total number of machines. This problem is subject to precedence, clustering, accessibility and capacity constraints among the features, operations, setups and workstations. The mathematical model and heuristic algorithm based on feature group strategy and polychromatic sets theory are presented to find an optimal solution. The feature group strategy and polychromatic sets theory are used to establish constraint model. A heuristic operations sequencing and assignment algorithm is given. An industrial case study is carried out, and multiple optimal solutions in different line configurations are obtained. The case studying results show that the solutions with shorter cycle time and higher line balancing rate demonstrate the feasibility and effectiveness of the proposed algorithm. This research proposes a heuristic line configuration and balancing algorithm based on feature group strategy and polychromatic sets theory which is able to provide better solutions while achieving an improvement in computing time.

Margin optimization algorithm for digital subscriber lines based on particle swarm optimization

The margin maximization problem in digital subscriber line（DSL） systems is investigated.The particle swarm optimization（PSO） theory is applied to the nonconvex margin optimization problem with the target power and rate constraints.PSO is a new evolution algorithm based on the social behavior of swarms, which can solve discontinuous, nonconvex and nonlinear problems efficiently.The proposed algorithm can converge to the global optimal solution, and numerical example demonstrates that the proposed algorithm can guarantee the fast convergence within a few iterations.

A Line Complex-Based Evolutionary Algorithm for Many-Objective Optimization

In solving many-objective optimization problems(MaO Ps),existing nondominated sorting-based multi-objective evolutionary algorithms suffer from the fast loss of selection pressure.Most candidate solutions become nondominated during the evolutionary process,thus leading to the failure of producing offspring toward Pareto-optimal front with diversity.Can we find a more effective way to select nondominated solutions and resolve this issue?To answer this critical question,this work proposes to evolve solutions through line complex rather than solution points in Euclidean space.First,Plücker coordinates are used to project solution points to line complex composed of position vectors and momentum ones.Besides position vectors of the solution points,momentum vectors are used to extend the comparability of nondominated solutions and enhance selection pressure.Then,a new distance function designed for high-dimensional space is proposed to replace Euclidean distance as a more effective distancebased estimator.Based on them,a novel many-objective evolutionary algorithm(MaOEA)is proposed by integrating a line complex-based environmental selection strategy into the NSGAⅢframework.The proposed algorithm is compared with the state of the art on widely used benchmark problems with up to 15 objectives.Experimental results demonstrate its superior competitiveness in solving MaOPs.

Design of Underwater Robot Lines Based on a Hybrid Automatic Optimization Strategy

In this paper, a hybrid automatic optimization strategy is proposed for the design of underwater robot lines. Isight is introduced as an integration platform. The construction of this platform is based on the user programming and several commercial software including UG6.0, GAMBIT2.4.6 and FLUENT12.0. An intelligent parameter optimization method, the particle swarm optimization, is incorporated into the platform. To verify the strategy proposed, a simulation is conducted on the underwater robot model 5470, which originates from the DTRC SUBOFF project. With the automatic optimization platform, the minimal resistance is taken as the optimization goal;the wet surface area as the constraint condition; the length of the fore-body, maximum body radius and after-body＇s minimum radius as the design variables. With the CFD calculation, the RANS equations and the standard turbulence model are used for direct numerical simulation. By analyses of the simulation results, it is concluded that the platform is of high efficiency and feasibility. Through the platform, a variety of schemes for the design of the lines are generated and the optimal solution is achieved. The combination of the intelligent optimization algorithm and the numerical simulation ensures a global optimal solution and improves the efficiency of the searching solutions.

A Two-Layer Active Power Optimization and Coordinated Control for Regional Power Grid Partitioning to Promote Distributed Renewable Energy Consumption

With the large-scale development and utilization of renewable energy,industrial flexible loads,as a kind of loadside resource with strong regulation ability,provide new opportunities for the research on renewable energy consumption problem in power systems.This paper proposes a two-layer active power optimization model based on industrial flexible loads for power grid partitioning,aiming at improving the line over-limit problem caused by renewable energy consumption in power grids with high proportion of renewable energy,and achieving the safe,stable and economical operation of power grids.Firstly,according to the evaluation index of renewable energy consumption characteristics of line active power,the power grid is divided into several partitions,and the interzone tie lines are taken as the optimization objects.Then,on the basis of partitioning,a two-layer active power optimization model considering the power constraints of industrial flexible loads is established.The upper-layer model optimizes the planned power of the inter-zone tie lines under the constraint of the minimum peak-valley difference within a day;the lower-layer model optimizes the regional source-load dispatching plan of each resource in each partition under the constraint of theminimumoperation cost of the partition,so as to reduce the line overlimit phenomenon caused by renewable energy consumption and save the electricity cost of industrial flexible loads.Finally,through simulation experiments,it is verified that the proposed model can effectively mobilize industrial flexible loads to participate in power grid operation and improve the economic stability of power grid.

Evaluation of Multi-Temporal-Spatial Scale Adjustment Capability and Cluster Optimization Operation Method for Distribution Networks with Distributed Photovoltaics

Themassive integration of high-proportioned distributed photovoltaics into distribution networks poses significant challenges to the flexible regulation capabilities of distribution stations.To accurately assess the flexible regulation capabilities of distribution stations,amulti-temporal and spatial scale regulation capability assessment technique is proposed for distribution station areas with distributed photovoltaics,considering different geographical locations,coverage areas,and response capabilities.Firstly,the multi-temporal scale regulation characteristics and response capabilities of different regulation resources in distribution station areas are analyzed,and a resource regulation capability model is established to quantify the adjustable range of different regulation resources.On this basis,considering the limitations of line transmission capacity,a regulation capability assessment index for distribution stations is proposed to evaluate their regulation capabilities.Secondly,considering different geographical locations and coverage areas,a comprehensive performance index based on electrical distance modularity and active power balance is established,and a cluster division method based on genetic algorithms is proposed to fully leverage the coordination and complementarity among nodes and improve the active power matching degree within clusters.Simultaneously,an economic optimization model with the objective of minimizing the economic cost of the distribution station is established,comprehensively considering the safety constraints of the distribution network and the regulation constraints of resources.This model can provide scientific guidance for the economic dispatch of the distribution station area.Finally,case studies demonstrate that the proposed assessment and optimization methods effectively evaluate the regulation capabilities of distribution stations,facilitate the consumption of distributed photovoltaics,and enhance the economic efficiency of the distribution station area.

Global Convergence of an Extended Descent Algorithm without Line Search for Unconstrained Optimization

In this paper, we extend a descent algorithm without line search for solving unconstrained optimization problems. Under mild conditions, its global convergence is established. Further, we generalize the search direction to more general form, and also obtain the global convergence of corresponding algorithm. The numerical results illustrate that the new algorithm is effective.

Buffer allocation method of serial production lines based on improved ant colony optimization algorithm

Buffer influences the performance of production lines greatly.To solve the buffer allocation problem(BAP) in serial production lines with unreliable machines effectively,an optimization method is proposed based on an improved ant colony optimization(IACO) algorithm.Firstly,a problem domain describing buffer allocation is structured.Then a mathematical programming model is established with an objective of maximizing throughput rate of the production line.On the basis of the descriptions mentioned above,combining with a two-opt strategy and an acceptance probability rule,an IACO algorithm is built to solve the BAP.Finally,the simulation experiments are designed to evaluate the proposed algorithm.The results indicate that the IACO algorithm is valid and practical.

A New Nonlinear Conjugate Gradient Method for Unconstrained Optimization Problems

In this paper,an efficient conjugate gradient method is given to solve the general unconstrained optimization problems,which can guarantee the sufficient descent property and the global convergence with the strong Wolfe line search conditions.Numerical results show that the new method is efficient and stationary by comparing with PRP＋ method,so it can be widely used in scientific computation.

A Line Search Algorithm for Unconstrained Optimization

It is well known that the line search methods play a very important role for optimization problems. In this paper a new line search method is proposed for solving unconstrained optimization. Under weak conditions, this method possesses global convergence and R-linear convergence for nonconvex function and convex function, respectively. Moreover, the given search direction has sufficiently descent property and belongs to a trust region without carrying out any line search rule. Numerical results show that the new method is effective.

Designing and Optimization of an Off-line Programming System for Robotic Belt Grinding Process

Off-line programming （OLP） system becomes one of the most important programming modules for the robotic belt grinding process, however there lacks research on increasing the grinding dexterous space depending on the OLP system. A new type of grinding robot and a novel robotic belt grinding workcell are forwarded, and their features are briefly introduced. An open and object-oriented off-line programming system is developed for this robotic belt grinding system. The parameters of the trimmed surface are read from the initial graphics exchange specification （IGES） file of the CAD model of the workpiece. The deBoor-Cox basis function is used to sample the grinding target with local contact frame on the workpiece. The numerical formula of inverse kinematics is set up based on Newton＇s iterative procedure, to calculate the grinding robot configurations corresponding to the grinding targets. After the grinding path is obtained, the OLP system turns to be more effective than the teach-by-showing system. In order to improve the grinding workspace, an optimization algorithm for dynamic tool frame is proposed and performed on the special robotic belt grinding system. The initial tool frame and the interval of neighboring tool frames are defined as the preparation of the algorithm. An optimized tool local frame can be selected to grind the complex surface for a maximum dexterity index of the robot. Under the optimization algorithm, a simulation of grinding a vane is included and comparison of grinding workspace is done before and after the tool frame optimization. By the algorithm, the grinding workspace can be enlarged. Moreover the dynamic tool frame can be considered to add one degree-of-freedom to the grinding kinematical chain, which provides the theoretical support for the improvement of robotic dexterity for the complex surface grinding.

Length optimization of straight line connecting turnout on main line in high-speed railway station yard

Taking the development of high-speed railway in China as background, and referring to the dynamic theory and wheel-rail contact mode, dynamic analysis model was established, considering the setting position of straight lines and running conditions of train in high-speed railway station yard. Using the established model, and choosing vehicle lateral acceleration and wheel suspension as the evaluation indexes, dynamic characteristic of vehicle traveling in turnout and adjacent area on main line was analyzed, and effects on travelling safety and stability of train aroused by length variation of straight lines were calculated based on analyzing the damping rules of vibration. The results show that, a certain length of straight lines can alleviate the vibration aroused in turnout and curve(turnout), length of straight lines connecting turnouts in different sections on main line was proposed to meet the demand of traveling stability, and shortening or cancelation of straight line for the scale limitation of station yard has less influence on operation safety of train.

NEURAL NETWORK INTELLIGENT SYSTEM FOR THE ON-LINE OPTIMIZATION IN CHEMICAL PLANTS

A strategy of developing on-line optimization intelligent systems based on combiningflowsheeting simulation and optimization package with artificial neural networks(ANN)is presented inthis paper.A number of optimization cases for a certain chemical plant are obtained off-line byusing PROCESS-Ⅱ or other flowsheeting programming with optimization.Then,taking these cases astraining examples,we establish a neural network systems which can be used on-line as an optimizer toobtain setpoints from input data sampled from distributed control system through gross error detectionand data reconciliation procedures.Such an on-line optimizer possesses two advantages over nonlinearprogramming package:first of all,there is no convergence problem for the trained ANN to be usedonline;secondly,the frequency for setpoints updating is not limited because only algebraic calculationrather than optimization is required to be carried out on-line.Here two key problems ofimplementing ANN approaches to the on-line optimization